Vector-Vault-DB
A high-performance vector database written from scratch. The engine is C++17; the public interface is a Python extension built with pybind11. There is no dependency on an existing vector store — the index, distance kernels, allocator, and on-disk format are all implemented directly.
Features
- Approximate nearest-neighbor search over float32 vectors using either an HNSW graph or an IVF (inverted-file) index.
- SIMD-accelerated distance kernels (AVX-512) for Euclidean, cosine, and dot product, with a scalar fallback selected once at startup via CPU detection.
- Custom arena allocator that hands out 64-byte aligned blocks for vector storage and tracks per-collection usage.
- Memory-mapped persistence: a versioned, CRC-checked binary snapshot format. Saves are atomic (temp file + fsync + rename); loads map the vector region instead of copying it onto the heap.
- Python bindings with NumPy support and an exception hierarchy that mirrors the engine's error categories.
Documentation
Full documentation lives in docs/:
- Installation
- Quickstart
- Concepts — metrics, indexes, tuning, persistence
- Python API reference
- C++ API guide
Architecture
Python (vectorvault) pybind11 extension, NumPy interop, exceptions
│
▼
Engine collection registry, shared allocator + persistence
│
▼
Collection record store, validation, query orchestration
├── Index HNSW / IVF — graph + inverted-file ANN
├── DistanceCalculator AVX-512 kernels with scalar fallback
├── MemoryAllocator 64-byte aligned arena, per-collection accounting
└── PersistenceManager atomic save, mmap-backed load, CRC validation
A collection is guarded by a single readers-writer lock: reads (get, query) take a shared lock, mutations (insert, delete, build, save) take an exclusive lock, so index membership stays consistent with the record store.
Requirements
- A C++17 compiler (GCC, Clang, or MSVC)
- CMake >= 3.20
- Python >= 3.9 with NumPy (for the bindings)
- pybind11 (resolved by the build backend)
Catch2, RapidCheck, and Hypothesis are fetched automatically for the test builds.
Install
pip install -e ".[test]"
This builds the native extension through scikit-build-core and installs the
vectorvault package in editable mode.
Quickstart
import vectorvault as vv
engine = vv.Engine()
coll = engine.create_collection("documents", dim=128, metric="cosine")
coll.insert("doc-1", embedding_a, metadata={"title": "intro"})
coll.insert("doc-2", embedding_b)
coll.build_index("hnsw", m=16, ef_construction=200)
results = coll.query(query_vector, k=10, ef_search=64)
for record_id, distance in results:
print(record_id, distance)
engine.save("documents", "documents.vv")
restored = vv.Engine().load("documents.vv")
Metrics: "euclidean" ("l2"), "cosine", "dot" ("dot_product").
Index types: "hnsw", "ivf".
Errors surface as typed exceptions under vectorvault.VectorVaultError; several
also derive from a builtin (for example NotFoundError is a KeyError and
SnapshotNotFoundError is a FileNotFoundError).
On-disk format
A snapshot is a single file: a fixed header (magic, version, dimensionality, metric, index type, region offsets, CRC-64 of the content), the collection name, a record directory sorted by id with optional metadata, a 64-byte aligned vector region, and the serialized index. Records, metadata keys, and index nodes are written in a canonical order, so saving a collection, loading it, and saving again produces a byte-identical file. Loads validate existence, magic, version, and checksum before mapping the vector region.
Testing
C++ (Catch2 + RapidCheck):
cmake -S . -B build -DVECTORVAULT_BUILD_TESTS=ON -DVECTORVAULT_BUILD_PYTHON=OFF
cmake --build build
ctest --test-dir build --output-on-failure
Python (pytest + Hypothesis):
pytest
Correctness-critical behavior — distance accuracy against a reference, allocator accounting, query ordering, index membership, and the snapshot round-trip — is covered by property-based tests. A recall benchmark builds an index over 10,000 vectors and asserts mean recall@10 against an exact baseline.
Layout
include/vectorvault/ Public C++ headers
src/core/ Core engine implementation
src/python/ pybind11 module + vectorvault package
tests/cpp/ C++ tests (Catch2 + RapidCheck)
tests/python/ Python tests (pytest + Hypothesis)
Notes and limitations
- The HNSW index selects neighbours with the diversity heuristic from Malkov & Yashunin (SELECT-NEIGHBORS-HEURISTIC), applied both when linking a new node and when pruning an existing node whose adjacency list overflows. On uniform random data this measures mean recall@10 of ~0.96 at dim 32 and ~0.85 at dim 64 (m=16, ef_construction=200, ef_search=50); recall still tapers on harder, higher-dimensional distributions.
- The snapshot reader assumes a little-endian host (it reads float components directly from the mapping).
- AVX-512 is detected at runtime; on hosts without it the scalar kernels are used, which are also the accuracy reference.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for vector-vault-db 0.1.1
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